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To improve user engagement and provide better insights into machine learning datasets and model performance, I propose adding interactive data visualization tools to ML Nexus. This feature would let users upload their own datasets, visualize key metrics, and analyze model performance directly on the website. By using libraries like Plotly and Matplotlib, we could offer a range of interactive charts and plots, making data exploration and understanding more accessible and straightforward.
Core Features: Data Upload and Visualization: Allow users to upload datasets in popular formats (e.g., CSV, Excel) to explore and analyze directly on the site. Implement tools to visualize dataset features (e.g., histograms, scatter plots, bar charts) and identify trends or patterns.
Model Performance Metrics: Enable visualizations for model performance metrics, such as accuracy, precision, recall, and F1 score, making it easier for users to interpret results. Provide model learning curve plots to show training and validation progress over epochs, helping users understand overfitting or underfitting.
Confusion Matrix: Create an interactive confusion matrix tool that visualizes classification results, allowing users to identify common misclassifications and performance trends by class.
Feature Distributions and Correlations: Offer visualizations for feature distributions (e.g., histograms, density plots) and correlation heatmaps to help users explore feature relationships and potential data issues.
This feature would make ML Nexus a more powerful tool for data scientists and machine learning practitioners at all levels by simplifying dataset analysis and enabling insightful visualizations directly on the platform.